A new energy ship energy storage battery system and control method

Abstract

The present application provides a new energy ship energy storage battery system and a control method. In the present application, the first power increase time is obtained by detecting the power of the target capacitor bank, and the second power increase time of the target capacitor bank is predicted; the power density characteristics in the charging power density sequence are extracted and compared with the capacity increase characteristics of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; the target demand power of the energy storage battery system is determined based on the current ship navigation state and the static load power; the discharge state of the energy storage battery pack is adjusted according to the target demand power and the convection energy efficiency, and the power control parameters during charging the target capacitor bank are corrected according to the first power increase time and the second power increase time, so as to improve the response speed of the power controller when the power increase speed of the target capacitor bank is slow and improve the dynamic performance of the ship under the condition of frequent load fluctuations.

Description

Technical Field

[0001] This application relates to the technical field of battery energy storage, and more specifically, to a new energy ship energy storage battery system and a control method therefor. Background Art

[0002] New energy ships refer to ships driven by new clean energy or high-efficiency energy-saving technologies, aiming to reduce the use of traditional fossil fuels, reduce greenhouse gas emissions, improve energy efficiency, and reduce the impact on the environment. In recent years, with the gradual maturity of new energy power technologies such as lithium batteries and alcohol fuels, the clean transformation of ships has gradually become the popular direction in the development process of transportation and water transportation; ships using new energy power such as lithium batteries have been widely used worldwide due to their advantages such as less environmental pollution, low operating energy consumption, and low noise and comfort.

[0003] In the existing new energy ship energy storage battery system, a composite energy storage system is mainly composed of an energy storage battery and a super capacitor. The energy storage battery pack provides electrical energy for the super capacitor pack through a charging method, so that the super capacitor maintains a high power standby state. When the ship load changes frequently or the instantaneous high-power demand increases, the super capacitor can quickly discharge to provide instant power response for the load. When the load demand is low, the energy storage battery pack quickly replenishes the super capacitor with electricity, so as to ensure that the super capacitor is always in a responsive state. However, in the prior art, since the response speed of the energy storage battery during charging the super capacitor is slow, the power rise speed of the capacitor is very slow, which will affect the dynamic performance of the ship under the condition of frequent load fluctuations. Summary of the Invention

[0004] This application provides a new energy ship energy storage battery system and a control method therefor, which can improve the response speed of the power controller when the power rise speed of the target capacitor group is slow, thereby improving the dynamic performance of the ship under the condition of frequent load fluctuations.

[0005] In a first aspect, this application provides a power control method, which can be executed by a network device, or can be executed by a chip configured in the network device. This application does not make any limitation in this regard.

[0006] Specifically, the method includes:

[0007] Charging a target capacitor group through an energy storage battery pack, and detecting a charging power density sequence during the charging process;

[0008] Detecting a first power rise time of the target capacitor group, constructing a power density regression model through the charging power density sequence, and predicting a second power rise time of the target capacitor group through the power density regression model;

[0009] Extract the power density features in the charging power density sequence and compare them with the capacity increase features of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system;

[0010] Obtain the current ship navigation state and static load power, and determine the target demand power of the energy storage battery system based on the current ship navigation state and static load power;

[0011] Adjust the discharge state of the energy storage battery bank according to the target demand power and the convection energy efficiency, and correct the power control parameters when charging the target capacitor bank according to the first power increase time and the second power increase time.

[0012] Combined with the first aspect, in some implementation manners of the first aspect, constructing a power density regression model through the charging power density sequence specifically includes:

[0013] Perform forward difference on each charging power value in the charging power density sequence to obtain a charging power increase sequence;

[0014] Perform autoregressive analysis according to each charging power increase value and the corresponding time sequence in the charging power increase sequence to obtain a power density regression model.

[0015] Combined with the first aspect, in some implementation manners of the first aspect, predicting the second power increase time of the target capacitor bank through the power density regression model specifically includes:

[0016] Obtain the power density regression model and the charging time interval of the target capacitor bank;

[0017] Within the charging time interval of the target capacitor bank, perform power increase prediction according to the power density regression model to obtain the charging power increase values corresponding to different prediction times;

[0018] Take the maximum value among all the charging power increase values as the characteristic increase value, and determine the second power increase time based on the prediction time corresponding to the characteristic increase value.

[0019] Combined with the first aspect, in some implementation manners of the first aspect, extracting the power density features in the charging power density sequence specifically includes:

[0020] Normalize the charging power density sequence to obtain a normalized charging power density sequence;

[0021] Perform modal decomposition according to the normalized charging power density sequence to obtain the charging power density intrinsic mode function;

[0022] Based on the charging power density eigenmode function, power density feature extraction is performed to obtain the power density features corresponding to different times.

[0023] Combined with the first aspect, in some implementation manners of the first aspect, before extracting the power density features in the charging power density sequence and comparing them with the capacity increase feature of the target capacitor bank, it further includes: obtaining the capacity increase feature of the target capacitor bank.

[0024] Combined with the first aspect, in some implementation manners of the first aspect, obtaining the capacity increase feature of the target capacitor bank specifically includes:

[0025] Performing power detection on the target capacitor bank to obtain a power acquisition sequence;

[0026] After normalizing the power acquisition sequence, performing forward difference to obtain a capacity increase feature sequence, and performing equally spaced acquisition on the capacity increase feature sequence to obtain the capacity increase features corresponding to different times.

[0027] Combined with the first aspect, in some implementation manners of the first aspect, determining the target demand power of the energy storage battery system based on the current ship navigation state and the static load power specifically includes:

[0028] Obtaining the current ship navigation state to determine the ship propulsion power;

[0029] Determining the target demand power of the energy storage battery system according to the ship propulsion power and the static load power.

[0030] In a second aspect, the present application provides a new energy ship energy storage battery system, which includes a power control unit, and the power control unit includes:

[0031] A data detection module, the detection module is used to charge the target capacitor bank through the energy storage battery pack and detect a charging power density sequence during the charging process;

[0032] A data processing module, the data processing module is used to perform power detection on the target capacitor bank to obtain a first power increase time, construct a power density regression model through the charging power density sequence, and predict a second power increase time of the target capacitor bank through the power density regression model;

[0033] The data processing module is further used to extract the power density features in the charging power density sequence and compare them with the capacity increase features of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system;

[0034] The data processing module is further configured to obtain the current ship navigation state and the static load power, and determine the target required power of the energy storage battery system based on the current ship navigation state and the static load power;

[0035] A power control module, configured to adjust the discharge state of the energy storage battery pack according to the target required power and the convective energy efficiency, and correct the power control parameters when charging the target capacitor bank according to the first power increase time and the second power increase time.

[0036] In a third aspect, the present application provides a computer terminal device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above power control method.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium, which stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above power control method.

[0038] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects:

[0039] In a new energy ship energy storage battery system and control method provided by the present application, first, the energy storage battery pack charges the target capacitor bank, and a charging power density sequence is detected during the charging process; the first power increase time is obtained by detecting the power of the target capacitor bank, a power density regression model is constructed through the charging power density sequence, and the second power increase time of the target capacitor bank is predicted through the power density regression model; the power density characteristics in the charging power density sequence are extracted and compared with the capacity increase characteristics of the target capacitor bank to obtain the battery convective energy efficiency of the new energy ship energy storage battery system; the current ship navigation state and the static load power are obtained, and the target required power of the energy storage battery system is determined based on the current ship navigation state and the static load power; the discharge state of the energy storage battery pack is adjusted according to the target required power and the convective energy efficiency, and the power control parameters when charging the target capacitor bank are corrected according to the first power increase time and the second power increase time.

[0040] It can be seen that in this application, by detecting the charging power density sequence and establishing a power density regression model, the system can predict the power increase trend of the target capacitor bank; and based on the second power increase time predicted from the power increase trend, the charging response of the target capacitor bank can be estimated. When it is found that the actual power increase time (the first power increase time) is slow and the prediction model indicates that the charging of the target capacitor bank lags, the power control parameters during charging of the target capacitor bank are corrected to adjust the charging power in advance when the charging response is poor, ensuring that the system can quickly adapt to the load demand; and during the charging process of this application, by extracting the charging power density characteristics and comparing them with the capacity increase characteristics of the target capacitor bank, the system can obtain the battery convection energy efficiency in real time. When the load fluctuates frequently, the change in the battery convection energy efficiency can directly affect the charging speed and discharge capacity. Furthermore, a multi-level discharge state control is adopted, that is, the discharge state of the energy storage battery bank is adjusted according to the target demand power and the battery convection energy efficiency. When the load demand increases, the controller can immediately increase the discharge power to make up for the insufficient power of the capacitor bank. In addition, by controlling the output of the energy storage battery bank, the system can preferentially use the battery bank to provide instantaneous power when the charging speed of the capacitor bank is slow, thereby reducing the impact of load fluctuations on the ship power system.

[0041] In summary, through the multi-level discharge state control based on the target demand power and convection energy efficiency of the ship, and then by correcting the power control parameters according to the first power increase time and the second power increase time, this application can improve the response speed of the power controller when the power increase speed of the target capacitor bank is slow, and improve the dynamic performance of the ship under the condition of frequent load fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is an exemplary flowchart of a power control method shown according to some embodiments of this application;

[0043] Figure 2 is an exemplary process for determining the second power increase time in some embodiments of this application;

[0044] Figure 3 is a schematic structural diagram of a power control unit shown according to some embodiments of this application;

[0045] Figure 4 is a schematic structural diagram of a computer terminal device for implementing the power control method shown according to some embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] This application charges a target capacitor bank through an energy storage battery bank, and obtains a charging power density sequence during the charging process; detects the first power increase time of the target capacitor bank, constructs a power density regression model through the charging power density sequence, and predicts the second power increase time of the target capacitor bank through the power density regression model; extracts the power density characteristics in the charging power density sequence and compares them with the capacity increase characteristics of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; obtains the current ship navigation state and static load power, determines the target demand power of the energy storage battery system based on the current ship navigation state and static load power; adjusts the discharge state of the energy storage battery bank according to the target demand power and convection energy efficiency, and corrects the power control parameters when charging the target capacitor bank according to the first power increase time and the second power increase time, so as to be able to improve the response speed of the power controller when the power increase speed of the target capacitor bank is slow and improve the dynamic performance of the ship under the condition of frequent load fluctuations.

[0047] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the specification drawings and specific embodiments. Refer to Figure 1 , which is an exemplary flowchart of a power control method shown according to some embodiments of the present application. The power control method 100 mainly includes the following steps:

[0048] In step S101, charge the target capacitor bank through the energy storage battery bank, and obtain a charging power density sequence during the charging process.

[0049] It should be noted that the energy storage battery bank described in this application is a core device for storing electrical energy and providing power for the ship. The energy storage battery bank is composed of multiple battery units, and the units are combined in series or parallel to meet the ship's requirements for energy density and power output. The energy storage battery bank stores electrical energy from shore power or on-board power generation systems on the ship and provides power as needed during navigation to support the operation of the propulsion system, living facilities and various equipment; the target capacitor bank described in this application refers to a energy storage device group with supercapacitors as the core. The target capacitor bank usually forms a composite energy storage system together with the energy storage battery bank, and its main function is to provide a rapid power response when the ship load fluctuates frequently.

[0050] Optionally, in some embodiments, charging the target capacitor bank by the energy storage battery pack can be achieved by the following steps: Obtain the battery charging cycle, and within the battery charging cycle, charge the selected capacitor module in the target capacitor bank. It should be noted that the battery charging cycle is preset as a constant time value according to requirements, and the battery charging cycle is the interval time for charging the target capacitor bank by the energy storage battery pack. For example, in specific implementation, the battery charging cycle can be preset as 1 h and the charging time as 5 min, then at the start of each battery charging cycle, charge the selected capacitor module in the target capacitor bank according to the preset charging time.

[0051] It should be noted that the charging power density sequence includes multiple charging power values and corresponding acquisition times, and the charging power density sequence is used to reflect the charging speeds corresponding to different times. Optionally, in some embodiments, equally spaced power detection can be performed by a power sensor on the connection line between the output end of the energy storage battery pack and the input end of the target capacitor bank to obtain the charging power density sequence. In some other embodiments, it can also be achieved by other devices or equipment capable of power acquisition, which is not limited here.

[0052] In step S102, perform a power quantity detection on the target capacitor bank to obtain the first power quantity rising time, construct a power density regression model through the charging power density sequence, and predict the second power quantity rising time of the target capacitor bank through the power density regression model.

[0053] It should be noted that generally, as the remaining power of the capacitor decreases, its internal resistance usually increases. In this application, according to the relationship between the internal resistance of the capacitor and the remaining power of the battery, the power quantity of the target capacitor bank is estimated by measuring the internal resistance of the capacitor. Optionally, in some embodiments, performing a power quantity detection on the target capacitor bank to obtain the first power quantity rising time can be achieved by the following steps: Perform multiple internal resistance tests on the target capacitor bank at equal intervals, and determine the power quantity values of the target capacitor bank at different test times according to the preset power quantity mapping table from the internal resistance values corresponding to different test times. Take the difference between the test time corresponding to the maximum power quantity rise and the charging start time as the first power quantity rising time, where the charging start time is the start time of charging the target capacitor bank by the energy storage battery pack. In specific implementation, a temperature compensation unit can also be added when performing the power quantity detection on the target capacitor bank, that is, temperature compensation is added in the actual capacity detection. Since the capacity of the battery changes with temperature, obtain the current temperature of the battery through a temperature sensor and correct the capacity in combination with the temperature compensation model.

[0054] Optionally, in some embodiments, the voltage-electricity characteristic curve of the target capacitor bank may also be obtained. The voltage-electricity characteristic curve is calibrated through testing during the factory production of the target capacitor bank. By detecting the voltage between the capacitor plates of the target capacitor bank, a mathematical model between voltage and electricity is established based on the voltage-electricity characteristic curve to estimate the electricity, and the electricity value at different test times is obtained. The difference between the test time corresponding to the maximum value of the electricity increase and the starting time of charging is used as the first electricity increase time.

[0055] Optionally, in some embodiments, the construction of the power density regression model by the charging power density sequence may be implemented by the following steps:

[0056] Perform forward difference on each charging power value in the charging power density sequence to obtain a charging power increase sequence;

[0057] Perform autoregressive analysis based on each charging power increase value and its corresponding time sequence in the charging power increase sequence to obtain a power density regression model.

[0058] It should be noted that the function of the power density regression model is to predict the electricity increase behavior of the target capacitor bank under different power density conditions through the input charging power increase value data. In the process of constructing a power density regression model through the charging power increase value data, the regression model can adopt linear regression, non-linear regression or machine learning regression algorithms (such as support vector machine regression or neural network) to fit the power density change trend. Specifically, in implementation, a moving average autoregressive model can also be constructed based on each charging power increase value and its corresponding time sequence in the charging power increase sequence as the power density regression model.

[0059] The following gives a preferred embodiment of obtaining the power density regression model in this application:

[0060] First, preset the prediction period to 1 min. At this time, the charging power increase values of the target capacitor bank in the past 1 min can be recorded respectively to obtain a charging power increase sequence. In some other embodiments, the maintenance time period can also be preset to other time lengths; furthermore, a time series diagram of the charging power increase sequence can be drawn. The abscissa of the time series diagram corresponds to different times. Furthermore, an exponential transformation can be performed on the time series diagram of the charging power increase sequence to eliminate the trend of the variance changing with time in the time series diagram.

[0061] Secondly, according to the time series diagram of the charging power rising sequence, an autocorrelation coefficient diagram of the charging power rising value is plotted. In the autocorrelation coefficient diagram, the horizontal axis is the number of lag periods, and the vertical axis is the value of the autocorrelation coefficient. A partial autocorrelation coefficient diagram of the charging power rising value is plotted. In the partial autocorrelation coefficient diagram, the horizontal axis is the number of lag periods, and the vertical axis is the value of the partial autocorrelation coefficient.

[0062] According to the characteristics of the autocorrelation coefficient diagram and the partial autocorrelation coefficient diagram, the order of the model and the value range of the coefficients can be preliminarily determined. For example, an autocorrelation coefficient diagram can be plotted to observe whether the autocorrelation coefficient shows a truncated feature after a certain order. If the autocorrelation coefficient drops sharply and remains near 0 after a certain order, the order of the autoregressive model can be preliminarily determined; a partial autocorrelation coefficient diagram is plotted to observe whether the partial autocorrelation coefficient shows a truncated feature after a certain order. If the partial autocorrelation coefficient drops sharply and remains near 0 after a certain order, the order of the moving average model can be preliminarily determined.

[0063] In specific implementation, first, according to the autocorrelation coefficient diagram, the last significant autocorrelation coefficient can be found, which is the order of the autocorrelation model. For example, if the last significant autocorrelation coefficient in the autocorrelation coefficient diagram is at the 3rd order, the order of the autocorrelation model is 3; then, according to the partial autocorrelation coefficient diagram, the last significant partial autocorrelation coefficient is found, which is the order of the moving average model. For example, if the last significant partial autocorrelation coefficient in the partial autocorrelation coefficient diagram is at the 2nd order, the order of the moving average model is 2; finally, according to the autocorrelation coefficient diagram and the partial autocorrelation coefficient diagram, the order (p, q) of the autoregressive moving average model is determined. For example, if both the autocorrelation coefficient diagram and the partial autocorrelation coefficient diagram decay to zero after the 3rd order, the order of the autoregressive moving average model is (3, 3); then, according to the order of the autoregressive moving average model, appropriate parameters are selected by the least squares method to establish an autoregressive moving average model for the charging power rising sequence, and the power density regression model is obtained.

[0064] In specific implementation, for example, the least squares method can be used to estimate the parameters and perform significance tests on the autoregressive and moving average processes of the model. In some embodiments, the significance level of the significance test is 0.05, and finally, the most suitable autoregressive moving average model parameters are selected according to the Schwarz Bayesian criterion, so as to determine the final autoregressive moving average model of the charging power rising value.

[0065] It should be noted that the second power rising time is used to reflect the characteristic response time of the power rising prediction according to the power output of the energy storage battery pack. Optionally, in some embodiments, refer to Figure 2As shown, the figure is an exemplary process for determining the second power increase time in some embodiments of the present application. The second power increase time of the target capacitor bank predicted by the power density regression model can be achieved by the following steps:

[0066] In step S1021, obtain the power density regression model and the charging time interval of the target capacitor bank;

[0067] In step S1022, within the charging time interval of the target capacitor bank, perform a power increase prediction according to the power density regression model to obtain the charging power increase values corresponding to different prediction times;

[0068] In step S1023, take the maximum value of the charging power increase values as the characteristic increase value, and determine the second power increase time based on the prediction time corresponding to the characteristic increase value.

[0069] Specifically, the difference between the prediction time corresponding to the characteristic increase value and the charging start time can be used as the second power increase time. It should be noted that the difference between the actually detected power increase time (the first power increase time) and the predicted theoretical increase time (the second power increase time) in the present application is used to reflect the degree of dynamic response lag that occurs in the battery pack of the energy storage battery of the new energy ship during the charging process, that is, the delay time for the system to respond to power changes under actual working conditions.

[0070] In step S103, extract the power density characteristics in the charging power density sequence and compare them with the capacity increase characteristics of the target capacitor bank to obtain the battery convection energy efficiency of the energy storage battery system of the new energy ship.

[0071] Optionally, in some embodiments, the extraction of the power density characteristics in the charging power density sequence can be achieved by the following steps:

[0072] Normalize the charging power density sequence to obtain a normalized charging power density sequence;

[0073] Perform modal decomposition on the normalized charging power density sequence to obtain the intrinsic mode functions of the charging power density;

[0074] Extract power density characteristics based on the intrinsic mode functions of the charging power density to obtain the power density characteristics corresponding to different times respectively.

[0075] In specific implementation, the empirical mode decomposition method can be used to perform empirical mode decomposition on the normalized sequence of the charging power density to obtain a set of intrinsic mode function components, and then the mean value of all the intrinsic mode function components is used as the intrinsic mode function of the charging power density. Among them, the empirical mode decomposition of the normalized sequence of the charging power density by the empirical mode decomposition method can be implemented by the following steps: identify the local maximum and local minimum in the normalized sequence of the charging power density; obtain the upper envelope and the lower envelope by interpolating the local extreme points, and generate an intermediate sequence by taking the average; iteratively extract the intrinsic mode function until the original sequence is decomposed into several intrinsic mode function components and a residual term.

[0076] Optionally, in some embodiments, different power density features corresponding to different moments can be obtained by equally spaced sampling of the intrinsic mode function of the charging power density.

[0077] Optionally, in some embodiments, before extracting the power density features in the charging power density sequence and comparing them with the capacity rise feature of the target capacitor bank, it further includes: obtaining the capacity rise feature of the target capacitor bank.

[0078] Among them, the capacity rise feature of the target capacitor bank can be obtained by the following steps:

[0079] Perform power detection on the target capacitor bank to obtain a power acquisition sequence;

[0080] After normalizing the power acquisition sequence, perform forward difference to obtain a capacity rise feature sequence, and perform equally spaced sampling on the capacity rise feature sequence to obtain different capacity rise features corresponding to different moments.

[0081] In specific implementation, voltage and current sensors can be connected to the target capacitor bank, and the sensors collect sensor data according to the set sampling frequency. According to the measured voltage and current data and combined with the capacitance value of the super capacitor, the change in the electric quantity of the capacitor can be obtained through the built-in integration algorithm to obtain the power acquisition sequence. The capacity rise feature sequence contains multiple capacity rise values, and the capacity rise value is the normalized power difference value. The forward difference in this application refers to calculating the difference between two adjacent elements in the sequence to obtain a new difference sequence. Through the forward difference in this application, the changing trend in the sequence can be analyzed.

[0082] It should be noted that the battery convection energy efficiency described in this application is used to reflect the dynamic conversion efficiency of the battery of the new energy ship energy storage battery system during energy convection. Among them, the power density characteristic represents the power characteristic input by the energy storage battery pack, and the capacity increase characteristic represents the actual output response of the target capacitor bank (i.e., the energy absorption and storage efficiency). If there is a high linear correlation between the power density characteristic and the capacity increase characteristic, it can be explained that the change in the battery power output can be well absorbed and converted by the battery, the convection energy efficiency of the system is high, and the energy loss during capacitor charging under complex working conditions is less. In some embodiments, the Pearson correlation coefficient between the capacity increase characteristic and the power density characteristic corresponding to different times can be used as the battery convection energy efficiency.

[0083] During the charging process of this application, by extracting the charging power density characteristic and comparing it with the capacity increase characteristic of the target capacitor bank, the system can obtain the battery convection energy efficiency in real time. When the load fluctuates frequently, the change in the battery convection energy efficiency can directly affect the charging speed and discharge capacity. Based on this characteristic, the system can optimize the discharge state and power control parameters in real time to maximize the energy efficiency and reduce the energy loss and charging delay during the charging process.

[0084] In step S104, obtain the current ship navigation state and the static load power, and determine the target demand power of the energy storage battery system based on the current ship navigation state and the static load power.

[0085] It should be noted that the current ship navigation state includes the current operating conditions and modes of the ship, including information such as speed, acceleration, navigation direction, and environmental conditions of the navigation area (such as wind speed). The navigation state of the ship can be obtained in real time through a sensor system composed of GPS, an acceleration sensor, and a speed sensor. The static load power represents the basic power demand of the ship under the current navigation state, including the basic power provided for the propulsion system, navigation system, living facilities, etc.

[0086] Optionally, in some embodiments, determining the target demand power of the energy storage battery system based on the current ship navigation state and the static load power can be implemented by the following steps:

[0087] Obtain the current ship navigation state to determine the ship propulsion power;

[0088] Determine the target demand power of the energy storage battery system according to the ship propulsion power and the static load power.

[0089] Among them, the ship propulsion power can be calculated and determined in the following manner: the ship propulsion power = environmental impact power + k × ship speed^n, where k and n are constant coefficients calibrated according to the ship size and weight when calculating the ship propulsion power, and the environmental impact power can be determined by a multivariate mapping function based on factors such as wind speed value, wave height, and tidal flow rate, that is, P 环境影响 = f(wind speed value, wave height, tidal flow rate).

[0090] In some embodiments, the target demand power of the energy storage battery system can also be calculated and determined in the following manner: P 目标 = P 推进 + P 辅助负载 + P 环境影响 − P 其他电力来源 , where P 推进 is the power demand of the ship propulsion system; P 辅助负载 is the power demand of other auxiliary equipment on the ship such as lighting and communication equipment; P 环境影响 is the change in power demand caused by environmental factors such as wind speed, wave height, and tidal current; P 其他电力来源 is the power provided by other power sources on the ship (such as diesel engines or fuel cells), where the power demand of the ship propulsion system, the auxiliary load power, and the environmental impact power can be based on the above-mentioned.

[0091] Optionally, in some embodiments, the target demand power of the energy storage battery system is the sum of the ship propulsion power and the static load power.

[0092] In step S105, adjust the discharge state of the energy storage battery pack according to the target demand power and the convective energy efficiency, and correct the power control parameters when charging the target capacitor bank according to the first power increase time and the second power increase time.

[0093] It should be noted that in order to ensure that the energy storage system can fully meet the load demand when the ship power demand is large, and at the same time avoid insufficient power during the charging process of the target capacitor bank and affecting the power, the energy storage battery system in this application realizes fine control of the discharge process through the setting of multiple discharge states; the multiple discharge states can be mapped through the threshold interval between the target demand power and the convective energy efficiency to determine the specific operations in different discharge states. Optionally, in some embodiments, the power demand can be divided into multiple threshold intervals based on the target demand power and the convective energy efficiency, and the corresponding discharge states are mapped through different intervals to control the discharge amount and discharge speed of the battery pack. Specifically, when implemented, the energy storage battery pack has multiple discharge states, and different discharge states correspond to different numbers of batteries discharging simultaneously, so as to avoid damage to the ship power when the ship power demand is large and the charging power is insufficient when charging the target capacitor bank.

[0094] Optionally, in some embodiments, for example, the number of batteries to be enabled and the discharge mode are determined according to the combination of the current power demand and the energy efficiency:

[0095] Low power demand range + high energy efficiency range: The system enters the low discharge state. It may only require a small number of battery packs to participate in the discharge, meeting the basic load demand while avoiding energy waste.

[0096] Medium power demand range + medium energy efficiency range: The system enters the medium discharge state. A medium number of battery packs are enabled for discharge to ensure that the system can follow the load demand and avoid power shortage.

[0097] High power demand range + low energy efficiency range: The system enters the high discharge state, and at the same time, more battery units are enabled for discharge to meet the high load demand of the ship. In addition, to avoid losses caused by low energy efficiency, some optimization measures can be taken, such as adjusting the battery output frequency or selecting battery modules with higher energy efficiency to give priority to discharge.

[0098] In some embodiments, the discharge state of the energy storage battery pack in this application can be divided into multiple levels, and each level corresponds to different numbers of discharging batteries and output power settings, which are described as follows: First-level discharge state (low discharge): When the power demand is in a lower range, only a small number of battery units participate in the discharge to maintain basic power supply. Second-level discharge state (medium discharge): When the power demand reaches the medium range, the number of battery units is increased to ensure the smooth operation of the load. This state is suitable for operation under general cruising or medium load conditions. Third-level discharge state (high discharge): Under high load demand, more battery units are enabled, and at the same time, ensure that the charging power of the capacitor bank is sufficient without affecting the charging of the capacitor and the power output of the system. Fourth-level discharge state (extreme discharge): When the power demand reaches the highest range and the battery convection efficiency is low, the system can enter the extreme discharge state, enabling all battery units to ensure sufficient power output under large load conditions, but limited to short-term use to prevent battery loss caused by over-discharge.

[0099] Optionally, in some embodiments, the power control parameters for charging the target capacitor bank can be corrected according to the first power rise time and the second power rise time by the following steps:

[0100] Obtain the initial feedback gain of the PID controller when charging the target capacitor bank;

[0101] Obtain the first power rise time, the second power rise time, and a preset deviation standard value;

[0102] According to the first power rising time, the second power rising time, and a preset deviation standard value, correct the initial feedback gain of the PID controller.

[0103] It should be noted that the feedback gain of the PID controller includes: proportional gain Kp, integral gain Ki, and derivative gain Kd. Specifically, in implementation, the time deviation between the first power rising time and the second power rising time can be obtained first, and the ratio between the time deviation and the deviation standard value is used as an adjustment coefficient. Then, according to the adjustment coefficient, the feedback gain of the PID controller is adjusted. In some embodiments, the adjustment coefficient can be multiplied by the initial proportional gain Kp, integral gain Ki, and derivative gain Kd respectively to obtain the corrected feedback gain for power control of the charging of the target capacitor bank. By correcting the power control parameters when charging the target capacitor bank, the response speed of the controller can be increased when the power rising speed of the target capacitor bank is slow, thereby avoiding affecting the dynamic performance of the ship under the condition of frequent load fluctuations.

[0104] It should be noted that since load fluctuations will frequently affect the system power demand, when the power rising speed of the capacitor bank lags behind, the system will first correct the power control parameters when charging the target capacitor bank, thereby adjusting the response demand of the energy storage battery bank. In the control scheme, the current ship load and power demand are continuously monitored to ensure that even when the capacitor charging is slow, the battery bank can flexibly supplement the load demand, thereby improving the overall dynamic response performance of the ship. And through the combined control of the prediction model, feedback control, power density characteristic analysis, and multi-level discharge state, the present application can quickly respond when the power rising speed of the capacitor bank is slow, thereby avoiding affecting the power system of the ship due to load fluctuations. This method ensures that the system can flexibly respond under the condition of frequent fluctuations, maintaining the stable operation and efficient power output of the ship.

[0105] In addition, on the other hand of the present application, in some embodiments, the present application provides a new energy ship energy storage battery system, which includes a power control unit. Refer to Figure 3 , this figure is a schematic structural diagram of the power control unit according to some embodiments of the present application. The power control unit 200 includes: a data detection module 201, a data processing module 202, and a power control module 203, which are described as follows:

[0106] The data detection module 201, the data detection module 201 is used to charge the target capacitor bank through the energy storage battery bank and detect the charging power density sequence during the charging process;

[0107] A data processing module 202, which is used to detect the power of the target capacitor bank to obtain a first power increase time, construct a power density regression model through the charging power density sequence, and predict a second power increase time of the target capacitor bank through the power density regression model;

[0108] The data processing module 202 is further used to extract power density features from the charging power density sequence and compare them with the capacity increase features of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system;

[0109] The data processing module 202 is further used to obtain the current ship navigation state and static load power, and determine the target demand power of the energy storage battery system based on the current ship navigation state and static load power;

[0110] A power control module 203, which is used to adjust the discharge state of the energy storage battery pack according to the target demand power and the convection energy efficiency, and correct the power control parameters when charging the target capacitor bank according to the first power increase time and the second power increase time.

[0111] The above has introduced in detail an example of a new energy ship energy storage battery system and a control method provided by the embodiments of the present application. It can be understood that, correspondingly, in order to implement the above functions, the device includes corresponding hardware structures and / or software modules for executing each function.

[0112] Those skilled in the art should easily realize that, combining the units and algorithm steps of the examples described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Therefore, professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of the present application.

[0113] In addition, the present application also provides a computer terminal device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above power control method.

[0114] In some embodiments, refer to Figure 4 , this figure is a schematic structural diagram of a computer terminal device for implementing a power control method according to some embodiments of the present application. The power control method in the above embodiments can be passed through Figure 4It is implemented by the computer terminal device shown. The computer terminal device 300 includes at least one communication bus 301, a communication interface 302, a processor 303, and a memory 304.

[0115] The processor 303 can be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more are used to control the execution of a power control method in this application.

[0116] The communication bus 301 may include a path for transmitting information between the above components.

[0117] The memory 304 can be a read-only memory (ROM), or other types of static storage devices that can store static information and instructions, a random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this. The memory 304 can exist independently and be connected to the processor 303 through the communication bus 301. The memory 304 can also be integrated with the processor 303.

[0118] Among them, the memory 304 is used to store the program code for executing the solution of this application and is controlled by the processor 303 for execution. The processor 303 is used to execute the program code stored in the memory 304. The program code may include one or more software modules. The determination of the target demand power in the above embodiments can be implemented by one or more software modules in the program code in the processor 303 and the memory 304.

[0119] The communication interface 302 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0120] Optionally, the above computer terminal device 300 may further include a power supply 305 for supplying power to various devices or circuits in the real-time computer terminal device.

[0121] In a specific implementation, as an embodiment, the computer terminal device may include multiple processors, and each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0122] The above computer terminal device may be a general-purpose computer terminal device or a special-purpose computer terminal device. In a specific implementation, the computer terminal device may be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer terminal device.

[0123] In addition, in other aspects of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above power control method.

[0124] In summary, in a new energy ship energy storage battery system and control method disclosed in the embodiments of the present application, first, the energy storage battery pack charges the target capacitor bank, and a charging power density sequence is detected during the charging process; the first power increase time is obtained by detecting the power of the target capacitor bank, a power density regression model is constructed through the charging power density sequence, and the second power increase time of the target capacitor bank is predicted through the power density regression model; the power density characteristics in the charging power density sequence are extracted and compared with the capacity increase characteristics of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; the current ship navigation state and the static load power are obtained, and the target demand power of the energy storage battery system is determined based on the current ship navigation state and the static load power; the discharge state of the energy storage battery pack is adjusted according to the target demand power and the convection energy efficiency, and the power control parameters during charging the target capacitor bank are corrected according to the first power increase time and the second power increase time, so that the response speed of the power controller can be improved when the power increase speed of the target capacitor bank is slow, and the dynamic performance of the ship can be improved under the condition of frequent load fluctuations.

[0125] The above are only embodiments of the present application, and common general technical solutions or features in the solutions are not described in detail herein. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, several modifications and improvements can be made, which should also be regarded as the protection scope of the present application, and these will not affect the implementation effect of the present application and the practicability of the patent.

[0126] The protection scope claimed in the present application shall be subject to the content of its claims, and the specific implementation manners and the like recorded in the specification can be used to interpret the content of the claims. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A power control method for power control of an energy storage battery system of a new energy ship, characterized in that, The method includes the following steps: Charge a target capacitor bank with a energy storage battery pack, and detect a charging power density sequence during the charging process; Detect the first charge increase time of the target capacitor bank, construct a power density regression model through the charging power density sequence, and predict the second charge increase time of the target capacitor bank through the power density regression model; Extract the power density characteristics in the charging power density sequence and compare them with the capacity increase characteristics of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; Obtain the current ship navigation state and static load power, and determine the target demand power of the energy storage battery system based on the current ship navigation state and static load power; Adjust the discharge state of the energy storage battery pack according to the target demand power and the battery convection energy efficiency, and correct the power control parameters when charging the target capacitor bank according to the first charge increase time and the second charge increase time; The battery convection energy efficiency is used to reflect the dynamic conversion efficiency when the battery of the new energy ship energy storage battery system conducts energy convection. Among them, the power density characteristic represents the power characteristic input by the energy storage battery pack, and the capacity increase characteristic represents the actual output response of the target capacitor bank, that is, the energy absorption and storage efficiency. If there is a high linear correlation between the power density characteristic and the capacity increase characteristic, it means that the change in battery power output is well absorbed and converted by the battery, and the battery convection energy efficiency of the system is high, and the energy loss during capacitor charging under complex working conditions is less. The Pearson correlation coefficient between the capacity increase characteristics and the power density characteristics corresponding to different times is used as the battery convection energy efficiency; The current ship navigation state includes the current operating conditions and modes of the ship, including speed, acceleration, navigation direction, and environmental conditions of the navigation area. The current ship navigation state is obtained in real time through a sensor system composed of a GPS, an acceleration sensor, and a speed sensor. The static load power represents the basic power demand of the ship under the current navigation state, including the basic power provided for the propulsion system, navigation system, and living facilities.

2. The method according to claim 1, wherein Constructing a power density regression model through the charging power density sequence specifically includes: Perform forward difference on each charging power value in the charging power density sequence to obtain a charging power increase sequence; Perform autoregressive analysis according to each charging power increase value and the corresponding time sequence in the charging power increase sequence to obtain a power density regression model.

3. The method according to claim 1, wherein Predicting the second charge increase time of the target capacitor bank through the power density regression model specifically includes: Obtain the power density regression model and the charging time interval of the target capacitor bank; Within the charging time interval of the target capacitor bank, perform power increase prediction according to the power density regression model to obtain the charging power increase values corresponding to different prediction times; Take the maximum value among all the charging power increase values as the characteristic increase value, and determine the second charge increase time based on the prediction time corresponding to the characteristic increase value.

4. The method according to claim 1, wherein Extracting the power density features in the charging power density sequence specifically includes: Normalizing the charging power density sequence to obtain a normalized charging power density sequence; Performing modal decomposition on the normalized charging power density sequence to obtain the charging power density intrinsic mode function; Based on the charging power density intrinsic mode function, extracting the power density features to obtain the power density features corresponding to different times respectively.

5. The method according to claim 1, wherein Before extracting the power density features in the charging power density sequence and comparing them with the capacity increase features of the target capacitor bank, it further includes: obtaining the capacity increase features of the target capacitor bank.

6. The method according to claim 5, characterized in that, Obtaining the capacity increase features of the target capacitor bank specifically includes: Performing power detection on the target capacitor bank to obtain a power acquisition sequence; Normalizing the power acquisition sequence and then performing forward difference to obtain a capacity increase feature sequence, and performing equally spaced acquisition on the capacity increase feature sequence to obtain the capacity increase features corresponding to different times respectively.

7. The method according to claim 1, wherein Determining the target demand power of the energy storage battery system based on the current ship navigation state and static load power specifically includes: Obtaining the current ship navigation state to determine the ship propulsion power; Determining the target demand power of the energy storage battery system according to the ship propulsion power and the static load power.

8. A new energy ship energy storage battery system includes a power control unit, characterized in that, The power control unit includes: A data detection module, which is used to charge the target capacitor bank through the energy storage battery pack and detect the charging power density sequence during the charging process; A data processing module, which is used to perform power detection on the target capacitor bank to obtain the first power increase time, construct a power density regression model through the charging power density sequence, and predict the second power increase time of the target capacitor bank through the power density regression model; The data processing module is further used to extract the power density features in the charging power density sequence and compare them with the capacity increase features of the target capacitor bank to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; The data processing module is further used to obtain the current ship navigation state and static load power, and determine the target demand power of the energy storage battery system based on the current ship navigation state and static load power; A power control module, which is used to adjust the discharge state of the energy storage battery pack according to the target demand power and the battery convection energy efficiency, and correct the power control parameters when charging the target capacitor bank according to the first power increase time and the second power increase time; The battery convection energy efficiency is used to reflect the dynamic conversion efficiency of the energy convection of the battery in the energy storage battery system of the new energy ship. Among them, the power density characteristic represents the power characteristic input by the energy storage battery pack, while the capacity increase characteristic represents the actual output response of the target capacitor bank, that is, the energy absorption and storage efficiency. If there is a high linear correlation between the power density characteristic and the capacity increase characteristic, it indicates that the change in the battery power output is well absorbed and converted by the battery, the battery convection energy efficiency of the system is high, and the energy loss during capacitor charging under complex working conditions is small. The Pearson correlation coefficient between the capacity increase characteristic and the power density characteristic corresponding to different moments is used as the battery convection energy efficiency; The current ship navigation state includes the current operating conditions and modes of the ship, including speed, acceleration, navigation direction, and environmental conditions of the navigation area. The current ship navigation state is obtained in real time through a sensor system composed of a GPS, an acceleration sensor, and a speed sensor. The static load power represents the basic power demand of the ship in the current navigation state, including the basic power provided for the propulsion system, navigation system, and living facilities.

9. A computer terminal device, characterized in that, The computer terminal device includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the power control method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one computer program, characterized in that, The computer program is loaded and executed by the processor to implement the operations performed by the power control method according to any one of claims 1 to 7.

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